Learning as the unsupervised alignment of conceptual systems

Abstract

Concept induction requires the extraction and naming of concepts from noisy perceptual experience. For supervised approaches, as the number of concepts grows, so does the number of required training examples. Philosophers, psychologists and computer scientists have long recognized that children can learn to label objects without being explicitly taught. In a series of computational experiments, we highlight how information in the environment can be used to build and align conceptual systems. Unlike supervised learning, the learning problem becomes easier the more concepts and systems there are to master. The key insight is that each concept has a unique signature within one conceptual system (for example, images) that is recapitulated in other systems (for example, text or audio). As predicted, children’s early concepts form readily aligned systems. By assembling conceptual systems from real-word datasets of text, images and audio, Roads and Love propose that objects embedded within a conceptual system have a unique signature that allows for conceptual systems to be aligned in an unsupervised fashion.